EEG source localization analysis in epileptic children during a visual working-memory task
Evangelos Galaris, Ioannis Gallos, Ivan Myatchin, Lieven Lagae, Constantinos Siettos
EEEG
SOURCE LOCALIZATION ANALYSIS IN EPILEPTICCHILDREN DURING A VISUAL WORKING - MEMORY TASK . A P
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Galaris Evangelos
Dipartimento di Matematica e Applicazioni “Renato Caccioppoli”Universita’ di Napoli Federicco IINaples, Italy
Gallos Ioannis
School of Applied Mathematics and Physical SciencesNational Technical University of AthensAthens, Greece
Myatchin Ivan
Department of AnesthesiologySint-Trudo Regional HospitalSint-Truiden, Belgium
Lagae Lieven
Department of Development and RegenerationSection Paediatric Neurology, KULeuvenLeuven, Belgium
Siettos Constantinos
Dipartimento di Matematica e Applicazioni “Renato Caccioppoli”Universita’ di Napoli Federicco IINaples, Italy(Corresponding author, [email protected])May 25, 2020 A BSTRACT
We localize the sources of brain activity of children with epilepsy based on EEG recordings acquiredduring a visual discrimination working memory task. For the numerical solution of the inverseproblem, with the aid of age-specific MRI scans processed from a publicly available database, weuse and compare three regularization numerical methods, namely the standarized Low ResolutionElectromagnetic Tomography (sLORETA), the weighted Minimum Norm Estimation (wMNE) andthe dynamic Statistical Parametric Mapping (dSPM). We show that all three methods provide thesame spatio-temporal patterns of differences between epileptic and control children. In particular,our analysis reveals statistically significant differences between the two groups in regions of theParietal Cortex indicating that these may serve as “biomarkers" for diagnostic purposes and ultimatelylocalized treatment. K eywords Source Localization · Neuroimaging · Epilepsy · Children a r X i v : . [ q - b i o . N C ] M a y PREPRINT - M AY
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Epilepsy affects more than 65 million people worldwide while, approximately 1 out of 150 children is diagnosed withepilepsy during the first 10 years of their life [1]. Although many children self-heal before adulthood, it has been shownthat children with epilepsy confront various cognitive and behavioural problems such as problems in learning, attentionand memory capacity [2]. Thus, the systematic study of the brain (dys)functionalities of children with epilepsy, andultimately the development of efficient/targeted treatments is one of the most challenging problems in neuroscienceand beyond. Towards this aim, non-invasive neuroimaging techniques and in particular electroencephalograph (EEG)recordings are commonly used for clinical assessment [3–10]. However, an analysis at the scalp level does not giveinsight to the malfunctioning of the actual brain regions and/or their connectivity. On the other hand, fMRI analysiscan provide a better insight but it is limited by its low-time resolution. Thus, source localization, i.e. the identificationof brain regions from scalp/non-invasive recordings (usually EEG or MEG) has emerged a promising approach thatcan facilitate the analysis of brain activity as a clinical diagnostic tool [11, 12]. However, the source localizationproblem is an ill-defined problem and as such, it poses open questions regarding its robustness and in general thevalidity of the obtained results [13]. Thus, comparative studies between the various numerical methods that aspireto solve the source localization problem are critical [14, 15]. Toward this aim, Jatoi et al. [16] have compared thestandardized low resolution brain electromagnetic tomography (sLORETA) with the exact LORETA (eLORETA) basedon EEG recordings of a visual experiment on healthy subjects. Cincotti et al. [17] compared two techniques for sourcelocalization, namely the surface Laplacian and LORETA using EEG recordings from a group of Alzheimer diseasepatients and age-matched controls. Yao and Devald [18] compared the performances of several source localizationmethods on the basis of both simulated and experimental EEG data of somatosensory evoked potentials. Attal andSchwartz [19] compared the performance of three methods, namely the weighted minimum norm (wMNE), sLORETAand the dynamic statistical parameter mapping (dSPM) for the characterization of distortions in cortical and subcorticalregions using a realistic anatomical and electrophysiological model of deep brain activity. Seeland et al. [20] comparedwMNE, sLORETA and dSPM using EEG data taken from eight subjects performing voluntary arm movements.Regarding epilepsy, the majority of the studies have performed source localization with the aid of EEG-fMRI recordingsand/or simulated data approximating epileptic spatio-temporal patterns such as spikes and discharges. For example,Ioannides et al. [21] assessed the performance of two source localization methods, wMNE and eLORETA usingMEG signals of ictal and interictal epileptiform discharges in epilepsy and K-complexes. Chowdhury et al. [22]compared the performance of the coherent Maximum Entropy on the Mean (cMEM) and the 4th order Extended SourceMultiple Signal Classification (4-ExSo-MUSIC) using MEG and EEG synthetic signals mimicking normal backgroundand epileptic discharges. Hasan et al. [23] evaluated four algorithms (dSPM, wMNE, sLORETA and cMEM) usingsimulated data from a combined biophysical/physiological model used to generate interictal epileptic spikes as well asreal EEG data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focalepilepsy. Moeller et al. [15] provides a review of the studies that used EEG-fMRI recordings to assess different types ofepileptic form activity, underpinning the necessity for comparing with other methods including EEG source analysis.Fewer studies have dealt with source-level analysis and compared different source localization methods using EEGclinical data taken by children with epilepsy. Among these studies, Adebimpe et al. [24] performed source localizationusing eLORETA to investigate changes in functional connectivity in children with Benign rolandic epilepsy withcentrotemporal spikes using resting-state EEG recordings. Groening et al. [25] combined EEG–fMRI and EEG sourceanalysis to identify epileptogenic foci in children. Elshoff et al. [26] examined the efficiency of EEG-fMRI and EEGsource analysis to localize the point of seizure onset in children with refractory focal epilepsy.The above studies have focused mainly on the study of brain regions that are activated during seizure periods or beforetheir onset. Several other studies have also aimed at analyzing the emerged patterns during seizure periods. For example,Fergus et al. [27] used a supervised machine learning approach to classify seizure and non-seizure records using anopen dataset of seizured EEG signals from both children and adults.On the other hand, it has been shown, that studying epileptic seizure-free EEG recordings is of great importance assuch analysis can facilitate the identification of patients at risk of epilepsy and/or forecast forth-coming seizures (for adiscussion and review of ictal and interictal activity and their analysis see for example [10].Here, we perform a source-localization analysis of the brain activity of well-controlled epileptic children during a visualWorking Memory (WM) task. WM is commonly viewed as a functional integration system with limited capacity that isable to store information within a short-term register and simultaneously manipulate it on-line. Thus, WM is one of themost important components of information processing and its dysfunction leads to various problems in several cognitivefunctions including mental arithmetic [28], reading [29, 30], decision making [31] and reasoning [32]. Epilepsy affectsa lot the WM functioning as it has been shown by many studies [33–37].2
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25, 2020Here, for the solution of the inverse problem, we use three methods, namely, sLORETA [38], dSPM [39] and wMNE [40].A statistical comparative analysis between methods and groups (healthy children vs children with epilepsy) revealed thecrucial role of the Superior Parietal Lobule (SPL) and Inferior Parietal Lobule (IPL) at WM. Our findings are in linewith fMRI studies [41–44] that have shown that SPL and IPL are being involved in WM processing and thus can serveas a “biomarker" for identifying, monitoring and assessing epilepsy in children.
In the study group, 21 children with established childhood epilepsy (age 6—16 years old; mean 11.43 years, SD ± ± The event-related potentials study was done as part of video-EEG monitoring. A visual one-backmatching workingmemory task was performed: children observed a continuous stream of seven different figures presented one after theother in pseudorandom order at the middle of a computer monitor, which was located at a distance of 1.0m from thesubject’s eyes. Everyday figures were used (horse, wardrobe, jacket, cake, comb, bunch of grapes, hammer), white witha black contour on grey background, size 7.5 cm × ◦ (cid:48) × ◦ (cid:48) . Each stimulus was presentedfor 1.5 s, followed by a delay of 1.0 s, after which the next stimulus was presented. During the delay period a fixationpoint (dark-grey cross) was shown at the middle of the screen to facilitate eyes fixation. Any figure identical to the oneimmediately preceding it was defined as a target stimulus (probability 0.30). Children were asked to respond to alltargets by pressing a button with their dominant hand. Both accuracy and speed were stressed. The single experimentalblock contained 120 trials, 36 of which were targets. The duration of the block was 5 min. This is an easy workingmemory task, which was chosen to ensure a good level of participant’s performance.First, the electrode placement and impedance calibration was performed. After that, the experimental procedure wasdescribed to the child. The child was seated comfortably in a dimly lit registration room and was instructed to look at themiddle of the computer screen placed in front of him to avoid unnecessary eye movements; a fixation point (dark-greycross) was shown between figures to facilitate eye fixation. The child was also instructed to avoid movements to reducemuscle artifacts in the EEG signal. The instruction for the task was given directly before the task. During the exper-iment, no interaction with the experimenter was allowed during the task and the experimenter sat out of sight of the child. Nineteen Ag/AgCl electrodes (Technomed Europe) were placed according to the international 10-20 system at Fp1,Fp2, F3, F4, F7, F8, Fz, C3, C4, Cz, T3, T4, T5, T6, P3, P4, Pz, O1 and O2. Placement of additional four EOGelectrodes resulted in two EOG channels: horizontal EOG – two electrodes on the outer canthi of eyes, and verticalEOG – two electrodes above and below one eye. EOG channels allowed us to detect both vertical and horizontal eyemovements in order to effectively remove them from EEG recording during subsequent preprocessing of the signal (seebelow). Two linked mastoid electrodes were used as a reference. EEG was sampled at a frequency of 1000 Hz with 12bits A/D converter and amplified using a band-pass filter of 0.095 – 70 Hz. Notch filter was off. Registration of thedigital EEG was made using the software program BrainlaB 4.0 (OSG, Belgium). The impedance of all electrodes was3
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25, 2020monitored for each subject prior to recording and was always kept below 5 k Ω . Data pre-processing was performed offline using the EEGLAB v.5.02 toolbox (Matlab 7.0.4 platform) [45]. TheECG channel was factored out. Data were filtered with a 50 Hz digital low pass filter. Eye movement artifacts weremarked and removed from the continuous signal without affecting the signal itself using an ICA-based algorithm [7].EEG fragments containing movement artifacts as well as any epileptic activity were removed based on visual inspectionof the data. This resulted in an EEG signal clean from (eye) movement artifacts and epileptic activity, which was thenused for further analysis. Afterwards, the continuous EEG signal was epoched according to the type of stimulus (Targetand Non Target), with 200 ms pre-stimulus (delay period) and 400 ms poststimulus (presentation period of the secondstimulus, where the motor responses had not yet taken place). Omitted Target trials (i.e. trials without correct motorresponse) and committed Non Target trials (i.e. trials with a wrong motor response) were excluded from the analysis.We then performed a down-sampling at 500 Hz and we applied a baseline correction by subtracting the mean value ofthe 200 ms of the pre-stimulus period. Overall, we ended up with 92 datasets (21 epileptic × × Source localization aims at identifying the (unknown) sources of the brain from data taken usually from noninvasiveelectromagnetic recording (here: EEG recordings). Its solution involves a forward and an inverse problem. Theforward problem refers to the calculation of the electric potentials of the electrodes starting from a given electricalsource. The solution of the forward problem is related to the construction of a head model. The head model containsboth anatomical information and the conductivities of three layers, namely the skull, the cortex and the scalp [46].Anatomical images can be obtained experimentally with the aid of Magnetic Resonance Imaging (MRI) scans, whilevolume conduction models can be constructed using e.g. the Boundary Element Method (BEM) [47] or the FiniteElements Model method (FEM) [48]. The head model volume is tessellated into small-sized cubes, the voxels. Sourcesmay be associated to single voxels or clusters of voxels. Here, each voxel is asscociated to a single source. The re-lation between the scalp recordings and the discretized head model volume is performed using the linear matrix equation: V = Gx + (cid:15) , (1)where V is a known N × matrix which contains the time instances as recorded by each channel ( N is the number ofchannels), x is the unknown M × matrix of the intensities of the M sources ( M is the number of voxels).The matrix G , with dimensions N × M is the so-called lead field matrix that contains the information of the headgeometry and conductivities. G is known (from the solution of the so called forward problem (see e.g. in [49])) and isrelated with the head model [50]; (cid:15) reflects the noise in the measurements.The inverse problem is ill-defined, as there is an infinite number of combinations of positions and intensities that couldeffectively produce the electric potentials and magnetic fields measured. The general idea behind its solution is toexpress it as a linear optimization problem with regularization: ˆ x = min x ( || V − Gx || + k (cid:88) i =1 a i || W i x || p ) . (2)In the above, k is the number of regularization constraints (refelcting the a-priori physiological information); the matrix W , M × M , is a weighted matrix related to the imposed constraints; α i is the regularization parameter and denote theimportance of every constraint.For different choices of W , k and p (reflecting the type of the norm), we get different methods.Here, for our analysis, we used and compared three different [51] methods, namely the weighted Minimum NormEstimation (wMNE), the standarized Low Resolution Electromagnetic Tomography (sLORETA) and the dynamicStatistical Parametric Mapping (dSPM) that are described below.4 PREPRINT - M AY
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For W = I (the identity matrix) and p = 2 (the L − norm) in 2 we get the Minimum Norm Estimation (MNE) [52].MNE uses the mathematical assumption that the best solution, through the infinite set of solutions, is the one withthe minimum norm. Despite the fact that MNE was the first method used to extract a 3D distributed solution, thesimplicity of its assumption often leads to inadequate solutions. In particular, it has been shown, that this method failsin identifying deep sources [53]. Because of the minimum norm constraint, sources that are located in deep regions aremoved closer to the cortex.The wMNE method is a variation of the MNE that improves the problem of the mislocation of the deep sources. wMNEuses instead of the identity matrix, a diagonal matrix W c that contains the weighting factors. From the multiple choicesthat can be chosen as weighted factors, usually W c = diag ( || G i || ) (for i =1,...,M) is chosen [54]. Then, the uniquesolution is given by: x wMNE = LV, (3)where L = G T ( G T G + αW c ) − is called the inverse operator with dimensions ( M × N ). The Dynamic Statistical Parametric Mapping (dSPM) [39] is similar to the wMNE but uses a different regularization.dSPM computes the source estimates of the noise based on the noise covariance matrix C (cid:15) = αH and normalizes therows of the inverse operator. H = I − T T is the centering matrix and plays the role of the identity matrix in the measurement space. Then, fromequation 3, the source estimates of the noise form a diagonal matrix: C ˆ x = W dSP M = LC (cid:15) L T . (4)Thus, the dSPM solution is given by: x dSP M = L dSP M V, (5)where L dSP M = W dSP M L . sLORETA considers another source of variance, except from the covariance of the measurement noise C (cid:15) : thecovariance of the actual sources C x = I . Assuming that the activity of the actual sources and the noise of themeasurements are uncorrelated and based on the linear relation of equation 1, we have: C V = GC x G T + C (cid:15) = GG T + αH. (6)Substituting equation 6 to 3, and taking into account the linear relation of equation 3, we can estimate the variation ofthe estimated sources as: C ˆ x = LC V L T = L ( GG T + αH ) L T = G T ( GG T + αH ) − G. (7)The covariance of the estimated sources is equivalent to the Backus and Gilbert resolution matrix [55], which is givenby plugging equation 1 into 3 and substituting the inverse operator to get: ˆ x = LGx = G T ( GG T + αH ) Gx = Ax = C ˆ x x, (8)where A = LG is the resolution matrix.In this case, the solution is given by: x sLORET A = L sLORET A V, (9)where L sLORET A = AL . 5 PREPRINT - M AY
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In our study, we did not have individual MRI scans for each child that participated to the experiment. Thus, in the absenceof such specific information, we used age-specific MRI templates for children acquired from the “NeurodevelopmentalMRI database" [56–59]. The goal of this database is to provide for research purposes, exactly in the absence ofspecific MRI scans, a series of age-appropriate average MRI reference templates and related information. Eachtemplate was constructed using identical procedures to facilitate comparisons across lifespan. The database consists ofaverage templates (T1W and T2W), segmenting priors, and stereotaxic atlases [56]. The “Neurodevelopmental MRIDatabase” is available online ( http://jerlab.psych.sc.edu/NeurodevelopmentalMRIDatabase/ ). The data-base is publicly available to researchers upon request for clinical and experimental studies of normal and pathologicalbrain development. The data is shared under a Creative Commons Attribution-NonCommercial-Noderivs 3.0 UnportedLicense (CC BY-NC-ND 3.0; http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en$_$US ).Using this database, we were able to construct an “average" age-specific head model for each child taking into accountits age. For our study, we constructed 11 averaged head models (taking into account the database with head models ofchildren between 6 and 16 years old, i.e. one “average" head model per year). In table 1, we provide information aboutthe total number of MRI scans per age.
Age
27 27 46 46 62 31 37 34 32 32 34
10 19 16 15 11 30 13
Combined
37 27 56 46 72 31 47 34 42 32 44Table 1: Total number of scans per age for 1.5T, 3.0T and combined average MRI templates. All 1.5T MRIs and part of3.0T MRIs are included in the "Combined" column as in the original publications [56, 58]Here, for the construction of the head models, as skull conductivities are age-dependent [60], we used differentconductivities ratios (CR, cortex/skull) for every age-dependent model. The conductivity value for scalp and cortexwas set to the standard value of 0.33 S/m [61]. Table 2 presents analytically the different conductivity ratios for everyage [61].
Age CR
15 20 30 40 50 60Table 2: Conductivity ratios (cortex/skull) for every age-dependent head model. The standard conductivity value forscalp and cortex was set to 0.33 S/m [61].
For our analysis, we used the BrainStorm toolbox for matlab [62]. The source-reconstructed time series were obtainedby combining the EEG recordings with the appropriate (respect to the age of the subject) constructed MRI templates.From each template, we extracted three layers (scalp, inner skull, outer skull) and the source space (cortical surface).The number of vertices for each layer were set to 2562 vertices for each surface. Then, the volume conduction modelswere constructed in openMEEG software [63] which uses the BEM. The space resolution for the source model was setto 5124 voxels with fixed orientation perpendicular to the cortex surface.Thus, the time series at the source level were reconstructed using wMNE, dSPM and sLORETA. The noise wascomputed from the raw EEG data using the pre-stimulus period for baseline correction and then the noise covariancematrix was calculated. A parameter that has to be determined is the “signal to noise ratio" (SNR). In Brainstorm, thecomputation of SNR is performed as in the original MNE software of Hamalainen [64]. The signal covariance matrix is“whitened" by the noise covariance matrix and the square root of the mean of its spectrum yields the average amplitudeof SNR. The default value in Brainstorm is set to 3.The main results of source localization procedure are presented analytically at table 3. For our illustrations, we havesplit the time period to three main intervals: the pre-stimulus period [-200ms 0ms), the period exactly after the stimulus[0ms - 199ms] and the post-stimulus period [200ms - 400ms). Our analysis reveals similar results when applying thedifferent methods.In Table 4 we also provide the numerical residuals for each method (the L2-norm ( res = || V − G ˆ x || , where G is theforward operator and ˆ x the estimated amplitudes), as well as the corresponding values of the regularization terms.6 PREPRINT - M AY
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Time period Method CT CNT ET ENTpro-stimulus(-200ms - -1ms) wMNE -Right occipitallobe ( ∼
140 voxels) -Occipital lobe( ∼
100 voxels) -Right occipitallobe ( ∼
90 voxels) -Occipital lobe( ∼
80 voxels)dSPM -Right occipitallobe ( ∼
220 voxels) -Right occipitallobe ( ∼
150 voxels) -Right occipitallobe ( ∼
160 voxels) -Occipital lobe( ∼
190 voxels)sLORETA -Right occipitallobe( ∼
530 voxels) -Occipital lobe( ∼
230 voxels)-Left parietallobe ( ∼
150 voxels) -Right occipitallobe ( ∼
290 voxels) -Right occipitallobe ( ∼
270 voxels) exactly after stimulus0ms - 199ms wMNE -Occipital lobe( ∼
120 voxels) -Right occipitallobe ( ∼
100 voxels) -Occipital lobe( ∼
90 voxels) -Superior parietallobe ( ∼
50 voxels)-Occipital lobe( ∼
110 voxels)dSPM -Right occipitallobe ( ∼
330 voxels) -Right occipitallobe ( ∼
310 voxels) -Occipital lobe( ∼
240 voxels) -Superior parietallobe ( ∼
100 voxels)-Right occipitallobe ( ∼
330 voxels)sLORETA -Right occipitallobe ( ∼
590 voxels) -Occipital lobe( ∼
510 voxels) -Occipital lobe( ∼
450 voxels) -Superior parietallobe ( ∼
240 voxels)-Occipital lobe( ∼
500 voxels) post-stimulus200ms - 399ms wMNE -Parietal lobe( ∼
70 voxels) -Right parietallobe ( ∼
60 voxels) -Right parietallobe ( ∼
60 voxels) -Right parietallobe ( ∼
80 voxels)dSPM -Parietal lobe( ∼
180 voxels) -Parietal lobe( ∼
110 voxels) -Right parietallobe ( ∼
200 voxels) -Parietal lobe( ∼
190 voxels)sLORETA -Parietal lobe( ∼
390 voxels) -Parietal lobe( ∼
360 voxels) -Right parietallobe ( ∼
470 voxels) -Parietal lobe( ∼
500 voxels)
Table 3: Group averaged sources as obtained by the three methods: wMNE, dSPM and sLORETA. CT: Control Target,CNT: Control non-Target, ET: Epileptic Target, ENT: Epileptic non-Target.
Method Group Residuals ( µ V) Regularizationterm (nA-m)wMNE
CT 4.53 ± ± ± ± ± ± ± ± dSPM CT 3.92 ± ± ± ± ± ± ± ± sLORETA CT 3.12 ± ± ± ± ± ± ± ± After the data pre-processing and the implementation of the source-localization algorithms, as described in theprevious section, we got 92 time-series at the source space. Our data have a spatio-temporal structure: number of voxels(spatial dimension) and time points (time dimension). In order to perform two-sample T-tests for the identification ofstatistically significant differences in the activity of the sources among groups, we checked three basic assumptions. Inparticular, we checked if (1) the amplitude of the source signal at each voxel and at each time instant follows a normaldistribution among subjects in a group, (2) the variances of the amplitude of the source signal at each voxel and at eachtime instant of CT, ET and CNT,ENT are equal, and (3) the amplitudes of the source signal at each voxel and at eachtime instant are independent for CT, ET and CNT, ENT. 7
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25, 2020For the test of normality, we used the Shapiro-Wilk test [65]. The null hypothesis of the test is that a sample comesfrom a normal distribution. The test statistic reads: W = ( (cid:80) ni =1 α i x ( i ) ) (cid:80) ni =1 ( x i − ¯ x ) , (10)where x ( i ) is the i-th order statistic, ¯ x is the sample mean and coefficients α i are given by ( α , α , ..., α n ) = m T V − C .C is a vector norm C = || V − m || = ( m T V − V − m ) , m = ( m , m , ..., m n ) T are the expected values of the orderstatistics of independent and identically distributed random variables sampled from the standard normal distribution andV is the covariance matrix of those normal order statistics. F-tests were performed to validate the second assumption(i.e. the equality of the variances of the amplitude values of each voxel between ET and CT and between ENT andCNT).Thus, we tested for normality and equality of variances for each voxel and each time sample (i.e. we have performed atotal of . ×
600 = 3 . . t-tests). The level of significance was set to p < . , meaning that the risk of takinga false positive is 5% of the cases. Here, in order to deal with the multicomparison problem, we used the false discoveryrate (FDR) correction [66]. Following this procedure, the null hypothesis could not be rejected. Similarly, the F testsvalidated also the second assumption. The independence is reasonably assumed to hold true.Having guaranteed that the T-test can be applied, we proceeded with the comparisons ET vs CT and ENT vs CNT.The null hypothesis H for both comparisons was that the two groups have equal means regarding the emerged spatio-temporal activation at the source level. Again, we performed .E simultaneous two-sample T-tests with p < . level of significance. FDR was used to deal with the multicomparison problem. Another constraint that we added toavoid spurius and random effects was the one of the minimum duration of the activations. Thus, we excluded all thesignals that were statistically significantly for time intervals less than 50 ms. This statistical analysis revealed thatall methods gave relatively similar results. The pair T-test between ENT and CNT revealed a statistically significantdifference in the time-range of 170ms-230ms. In this time-range, the Superior Parietal Lobe (SPL) was activated morein the ENT group; the activation of the SPL was mostly at the right hemisphere (figure 1). The pair T-test between ETand CT revealed a statistically significant difference in the time-range of 160ms-360ms. In this time-range the InferiorParietal Lobule (IPL) was activated more in the ET group (only at the right hemisphere) (figure 2).The above findings are summarized in Table 5. Comparison Time period Group activatedmore Brain region Brodman area Number of voxels CommentsENT vs CNT ∼
80 Only at the righthemisphere
ET vs CT ∼
170 Only at the righthemisphere
Table 5: Analytical presentation of the differences between the comparisons ET vs CT and ENT vs CNT.8
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25, 2020 (a) wMNE (b) dSPM (c) sLORETAFigure 1: ENT vs CNT: SPL mainly of the right hemisphere activate more for the ENT group at the time interval from170 to 230 ms.
Sensor-level analysis does not provide information about the actual sources that are involved in brain activity. From amathematical point of view, the problem of source localization from scalp recordings is an inverse ill-defined problemand as such, different types of regularization approaches may in principle result to different solutions. Thus, especiallyfor clinical assessment of brain neurological disorders such as epilepsy, there is a need for comparing and assessing therobustness of such methods.This is the first study to perform a comparative analysis of three numerical methods, namely the sLORETA, wMNE anddSPM to identify differences at the source level between healthy children and children with well-controlled epilepsy(i.e. in the absence of seizures) during a working memory task . In the absence of anatomical MRI scans, we used thepublicy “Neurodevelopmental MRI database" that provides age-specific average MRI templates. Our analysis showsthat all three methods yield essentially the same results, thus providing adequate confidence for our findings. Morespecifically, our analysis revealed consistent differences between the two groups in the parietal lobes. Importantly, ourfindings are in line with other studies investigating abnormalities of the brain function due to epilepsy with the use ofanatomical MRI, fMRI and EEG recordings [67–76]. More specifically, regarding children with epilepsy, Besenyeiet al. [76] used anatomical MRI and resting state EEG recordings to identify the abnormal brain activity in childrenwith benign rolandic epilepsy. Using LORETA, they found an increase activity, compared to controls, in the temporaland inferior parietal lobule. Other studies that have investigated the abnormal activity in children with epilepsy in thepresence of seizures have also pinpointed the importance of these areas. Clemens et al. ( [77] find increased activity in9
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25, 2020 (a) wMNE (b) dSPM (c) sLORETAFigure 2: ET vs CT: IPL of the right hemisphere activates more for the ET group at the time interval from 160 to 360ms.the superior perietal lobe children with benign childhood epilepsy with rolandic spikes, using MRI scans and restingstate EEG recordings. For the source localization analysis they used LORETA.Importantly, our study and findings reveal also the importance and potential that originates from the use of publiclyavailable scientific resources such as the “Neurodevelopmental MRI" database, which allow to the researchers tore-analyse available neuroimaging data and investigate questions beyond the scope of the original studies. This carries,in principle, the potential to gain new insights without the need to perform new from scratch, time-consuming andexpensive experiments.
Acknowledgments
E.G. was supported by a Ph.D. fellowship by the Department of Mathematics and Applications, University of NaplesFederico II and I.G. was supported by a Ph.D. fellowship by the National Technical University of Athens.
Conflict of interest
All authors declare no conflicts of interest in this paper 10
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